A machine learning-augmented aerodynamic database of rectangular cylinders

Author:

Li Yuerong1ORCID,Yan Lei1ORCID,Gao Huanxiang1ORCID,Hu Gang12ORCID

Affiliation:

1. Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology 1 , Shenzhen 518055, China

2. Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Harbin Institute of Technology 2 , Shenzhen 518055, China

Abstract

Rectangular cylinders submerged in a fluid encounter intricate aerodynamic forces, and the forces significantly influence the stability and safety of structures possessing a rectangular cross section. Although aerodynamic characteristics of these cylinders have been extensively studied, a comprehensive database cataloging these characteristics remains absent. This study conducted a large number of wind tunnel pressure testings to establish an aerodynamic database for rectangular cylinders with 2470 distinct configurations, including turbulent intensities ranging from 1% to 20%, side ratios ranging from 0.6 to 5, and wind attack angles ranging from 0° to 90°. The accuracy of the database was validated by data from the literature and wind tunnel force measurement experiments. More importantly, machine learning models were developed and have substantially expanded the experimental data, resulting in a comprehensive, continuous aerodynamic database for rectangular cylinders. By evaluating the model performance and verifying its generalization capability, the accuracy of the machine learning-augmented database is proved. This database is anticipated to serve as a critical reference for academic research and a practical reference for engineering applications.

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Commission

Guangdong Provincial Key Laboratory of Intelligent and Redilient Structures for Civil Engineering

Publisher

AIP Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3